Dholes-inspired optimization (DIO): a nature-inspired algorithm for engineering optimization problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260597" target="_blank" >RIV/61989100:27240/25:10260597 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10586-025-05543-2" target="_blank" >https://link.springer.com/article/10.1007/s10586-025-05543-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10586-025-05543-2" target="_blank" >10.1007/s10586-025-05543-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dholes-inspired optimization (DIO): a nature-inspired algorithm for engineering optimization problems

Popis výsledku v původním jazyce

This paper proposes the Dhole-Inspired Optimization (DIO) algorithm, a novel metaheuristic inspired by the cooperative hunting behavior of dholes (Cuon alpinus). The algorithm employs a hierarchical pack structure, where a Lead Vocalizer guides the search process while subordinate members adapt their movements to balance exploration and exploitation dynamically. This structure enhances search efficiency, prevents premature convergence, and improves solution accuracy across different problem landscapes. DIO is benchmarked on unimodal, multimodal, and composite test functions, demonstrating superior performance compared to established optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), Fast Evolutionary Programming (FEP), and Differential Evolution (DE). The results show that DIO achieves higher accuracy and faster convergence rates on a majority of test cases, validating its robustness and reliability in tackling complex optimization problems. Furthermore, the algorithm is evaluated on real-world engineering applications, demonstrating its adaptability and effectiveness in practical scenarios. The findings highlight DIO as a versatile and competitive optimization approach, suitable for a wide range of applications in science and engineering.

Název v anglickém jazyce

Dholes-inspired optimization (DIO): a nature-inspired algorithm for engineering optimization problems

Popis výsledku anglicky

This paper proposes the Dhole-Inspired Optimization (DIO) algorithm, a novel metaheuristic inspired by the cooperative hunting behavior of dholes (Cuon alpinus). The algorithm employs a hierarchical pack structure, where a Lead Vocalizer guides the search process while subordinate members adapt their movements to balance exploration and exploitation dynamically. This structure enhances search efficiency, prevents premature convergence, and improves solution accuracy across different problem landscapes. DIO is benchmarked on unimodal, multimodal, and composite test functions, demonstrating superior performance compared to established optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), Fast Evolutionary Programming (FEP), and Differential Evolution (DE). The results show that DIO achieves higher accuracy and faster convergence rates on a majority of test cases, validating its robustness and reliability in tackling complex optimization problems. Furthermore, the algorithm is evaluated on real-world engineering applications, demonstrating its adaptability and effectiveness in practical scenarios. The findings highlight DIO as a versatile and competitive optimization approach, suitable for a wide range of applications in science and engineering.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cluster Computing-The Journal of Networks Software Tools and Applications

ISSN

1386-7857

e-ISSN

1573-7543

Svazek periodika

28

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

38

Strana od-do

nestránkováno

Kód UT WoS článku

001576153100032

EID výsledku v databázi Scopus

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